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Coupled with the availability of large scale datasets, deep learning architectures have enabled rapid progress on the Question Answering task.
Ctrl: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R Varshney, Caiming Xiong, and Richard Socher. 2019 · 1909
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Korquad1. 0: Korean qa dataset for machine reading comprehension
Seungyoung Lim, Myungji Kim, and Jooyoul Lee. 2019 · 1909
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
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Harvesting paragraph-level question-answer pairs from Wikipedia
Xinya Du and Claire Cardie. 2018 · 1917
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Answering english questions by computer: a survey
Robert F Simmons. 1965 · 1965
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The pagerank citation ranking: Bringing order to the web
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999 · 1999
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Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers
Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. 2020 · 2002
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Xtreme: A massively multilingual multi-task benchmark for evaluating cross-lingual generalization
Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, and Melvin Johnson. 2020 · 2003
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020b · 2005
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Querent intent in multi-sentence questions
Laurie Burchell, Jie Chi, Tom Hosking, Nina Markl, and Bonnie Webber. 2020 · 2010
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The first question generation shared task evaluation challenge
Vasile Rus, Brendan Wyse, Paul Piwek, Mihai Lintean, Svetlana Stoyanchev, and Christian Moldovan. 2010 · 2010
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Toward stance-based personas for opinionated dialogues
Thomas Scialom, Serra Sinem Tekiroglu, Jacopo Staiano, and Marco Guerini. 2020 · 2010
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Multilingual synthetic question and answer generation for cross-lingual reading comprehension
Siamak Shakeri, Noah Constant, Mihir Sanjay Kale, and Linting Xue. 2020 · 2010
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A human judgement corpus and a metric for arabic mt evaluation
Houda Bouamor, Hanan Alshikhabobakr, Behrang Mohit, and Kemal Oflazer. 2014 · 2014
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Learning to ask: Neural question generation for reading comprehension
Xinya Du, Junru Shao, and Claire Cardie. 2017 · 2017
Cited alongside, same era.
Question generation for question answering
Nan Duan, Duyu Tang, Peng Chen, and Ming Zhou. 2017 · 2017
Cited alongside, same era.
Generating synthetic time series to augment sparse datasets
Germain Forestier, François Petitjean, Hoang Anh Dau, Geoffrey I Webb, and Eamonn Keogh. 2017 · 2017
Cited alongside, same era.
Two-stage synthesis networks for transfer learning in machine comprehension
David Golub, Po-Sen Huang, Xiaodong He, and Li Deng. 2017 · 2017
Cited alongside, same era.
Why we need new evaluation metrics for NLG
Jekaterina Novikova, Ondřej Dušek, Amanda Cercas Curry, and Verena Rieser. 2017 · 2017
Cited alongside, same era.
NewsQA: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017 · 2017
Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
Later among the works it cites.
DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
Later among the works it cites.
ELI5: Long form question answering
Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. 2019 · 2019
Later among the works it cites.
Antique: A non-factoid question answering benchmark
Helia Hashemi, Mohammad Aliannejadi, Hamed Zamani, and W. Bruce Croft. 2019 · 2019
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Cross-lingual training for automatic question generation
Vishwajeet Kumar, Nitish Joshi, Arijit Mukherjee, Ganesh Ramakrishnan, and Preethi Jyothi. 2019 · 2019
Later among the works it cites.
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Cited alongside, same era.
Neural question generation from text: A preliminary study
Qingyu Zhou, Nan Yang, Furu Wei, Chuanqi Tan, Hangbo Bao, and Ming Zhou. 2017 · 2017
Cited alongside, same era.
QuAC: Question answering in context
Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. 2018 · 2018
Cited alongside, same era.
Neural learning for question answering in italian
Danilo Croce, Alexandra Zelenanska, and Roberto Basili. 2018 · 2018
Cited alongside, same era.
PhotoshopQuiA: A corpus of non-factoid questions and answers for why-question answering
Andrei Dulceanu, Thang Le Dinh, Walter Chang, Trung Bui, Doo Soon Kim, Manh Chien Vu, and Seokhwan Kim. 2018 · 2018
Cited alongside, same era.
The NarrativeQA reading comprehension challenge
Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2018 · 2018
Cited alongside, same era.
BLEU is not suitable for the evaluation of text simplification
Elior Sulem, Omri Abend, and Ari Rappoport. 2018 · 2018
Cited alongside, same era.
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
Later among the works it cites.
Self-attention architectures for answer-agnostic neural question generation
Thomas Scialom, Benjamin Piwowarski, and Jacopo Staiano. 2019 · 2019
Later among the works it cites.
On the cross-lingual transferability of monolingual representations
Mikel Artetxe, Sebastian Ruder, and Dani Yogatama. 2020 · 2020
Closest in time.
Albumentations: fast and flexible image augmentations
Alexander Buslaev, Vladimir I Iglovikov, Eugene Khvedchenya, Alex Parinov, Mikhail Druzhinin, and Alexandr A Kalinin. 2020 · 2020
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Cross-lingual natural language generation via pre-training
Zewen Chi, Li Dong, Furu Wei, Wenhui Wang, Xian-Ling Mao, and Heyan Huang. 2020 · 2020
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Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
Closest in time.
Project PIAF: Building a native French question-answering dataset
Rachel Keraron, Guillaume Lancrenon, Mathilde Bras, Frédéric Allary, Gilles Moyse, Thomas Scialom, Edmundo-Pavel Soriano-Morales, and Jacopo Staiano. 2020 · 2020
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Reference and document aware semantic evaluation methods for korean language summarization
Dongyub Lee, Myeongcheol Shin, Taesun Whang, Seungwoo Cho, Byeongil Ko, Daniel Lee, Eunggyun Kim, and Jaechoon Jo. 2020 · 2020
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CamemBERT: a tasty French language model
Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric de la Clergerie, Djamé Seddah, and Benoît Sagot. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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